Predicting litter and live herb fuel consumption during prescribed fires in native and old-field upland pine communities of the southeastern United States
Bibliographic record
Abstract
The ability to predict fuel consumption during fires is essential for a wide range of applications, including estimation of fire effects and fire emissions. This project identified predictors of fuel consumption for the dominant fuel bed components (litter (<0.6-cm diameter dead material) and live herbs) during 217 prescribed fires in native longleaf pine ( Pinus palustris Mill.) and old-field loblolly pine ( Pinus taeda L.) – shortleaf pine ( Pinus echinata Mill.) communities in the southeastern United States. Additionally, these data were used to validate the First Order Fire Effects Model (FOFEM) fuel consumption computer model using custom and default fuel loads. Regression models using empirical data suggested that litter and live herb fuel consumption can be predicted by prefire litter and live herb fuel loads, litter and live herb fuel moisture, litter fuel bed bulk density, season of burn, years since fire, days since last rain ≥0.64 cm, relative humidity, energy release component, community type, pine and hardwood basal areas, and the Keetch–Byram drought index. FOFEM’s prediction of fuel consumption for litter, live herbs, and duff combined using default fuel loads was 1.5 times the measured fuel consumption (where duff fuel load was zero). Refinement of FOFEM’s fuel load and consumption calculations in the studied community types using the newly collected data and suggestions for model improvement would provide more accurate air quality inventories and assist in guiding appropriate regulation of prescribed fire.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".